All publications herein are incorporated by reference to the same extent as if each individual publication or patent application was specifically and individually indicated to be incorporated by reference. The following description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
Ovarian cancer is the leading cause of gynecologic cancer deaths in the United States. Despite similarities in initial disease presentation, the existence of molecular subtypes of ovarian cancer is suggested by clinical outcomes displaying a broad range of survival end points. For example, some patients develop a chronic-type disease that can be maintained on chemotherapy for more than five years. Others are intrinsically resistant to chemotherapy or initially respond to chemotherapy, but then rapidly become resistant to the treatment and subsequently have low response rates to other second-line agents. Strategic approaches customized for treating these different patient groups is lacking, as ovarian cancer therapy is implemented on a watch-and-wait basis. No diagnostic tool exists that distinguishes among these patient groups, such as identifying those patients responsive to chemotherapy, others that develop preliminary or long-term chemoresistance, or those likely to experience relapse of disease. Consequently, there is a critical need for 1) prognostic and/or diagnostic classifiers that can reliably distinguish among molecular subtypes of gynecological cancer such as ovarian cancer; and 2) novel treatment therapies accounting for these differences in molecular subtypes.
The development of effective prognostic, diagnostic and treatment strategies must account for molecular abnormalities motivating the underlying pathophysiology of the disease. Thus, an initial step for developing such tools first requires identification and classification of specific molecular abnormalities associated with particular disease conditions. These biomarkers can be readily applied for early detection, and prognostication, which can guide development of personalized therapies. Reliable tests that identify patient molecular subtypes not only improves clinical management options, but also provides early warning indicators to enroll high risk patients in increasingly available clinical trials and the latest personalized treatment strategies.
Moreover, detection of disease subtypes at the molecular level allows one to take advantage of recent discoveries in cancer research, which would ordinarily fall outside the detection capabilities of traditional clinical assessments. For example, recent studies have highlighted the importance of cancer stem cells (CSCs) in tumor formation and chemoresistance. These CSCs possess the hallmark “stemness” capacity for self-renewal and the proliferative ability to drive continued expansion, along with the differentiation capacity for neoplastic formation. It is notable that CSCs could represent less than 1% of the overall cell population in a tumor, yet provide crucial biochemical machinery powering the rapid growth and development of malignant cells in tumors. The existence of such rare and transient cell populations thus requires development of detection and classification approaches at the molecular level.
Described herein are gene signatures providing prognostic, diagnostic, treatment and molecular subtype classifications of ovarian cancers. Biostatistical methods are applied across a variety of studies encompassing a wide array of laboratory and clinical variables, thereby leading to generation of ovarian cancer disease signatures (OCDSs) that account for molecular heterogeneity present in gynecological cancers. Statistical analysis across multiple independent data sets allows generation of a preliminary ovarian cancer fixed signature (OCFS), a comprehensive definition of the core programming of disease development. Also described herein is a specific biochemical definition of an ovarian cancer stem cell identified via an (OCSC) signature. Finally, the development of ovarian cancer cell lines, including ovarian cancer stem cell lines (OCSC), and selectively labeled animal models provides in vitro and in vivo models for applying the aforementioned signatures to develop clinical applications, such personalized treatment strategies focused on molecular subtypes of gynecological cancers.